CIOMS cited

This review synthesizes global and Indian perspectives on real world evidence (RWE) for signal detection, outlines current frameworks, evaluates available real world data sources, and examines emerging methods including artificial intelligence/machine learning. It also discusses operational, regulatory, and ethical challenges in India and proposes a roadmap for strengthening RWE-based pharmacovigilance (PV) systems. By consolidating insights from open-access scientific literature and regulatory guidance, this review aims to support PV scientists, regulators, and healthcare policymakers in harnessing RWE to enhance drug safety and public health in India

This open access book adopts two approaches developed in political philosophy -- "ideal" theory and "non-ideal" theory -- to develop a new approach to bioethics. It argues that both ideal theory and non-ideal theory may be preferable depending on the situation. On this basis, it proposes a new decision-making approach using five criteria to analyse different situations and to decide which one to choose. This approach is introduced theoretically and considered practically using current examples such as reproductive rights, the Covid pandemic. This book will be of interest to researchers, students, and practitioners in the fields of bioethics, medical ethics, public health, and gender studies.

The increasing dependence of pharmaceutical research on personal and sensitive health data has elevated the importance of effective data protection frameworks within clinical and biomedical research environments. This paper critically examines the regulatory compliance landscape governing data protection in pharmaceutical research in Nigeria, situating the discussion within both domestic legal frameworks and international best practices.

Artificial intelligence (AI) is increasingly used in health-related research to generate knowledge, insights and technologies, but its application may challenge established ethical principles and human rights protections and create novel risks that existing oversight systems do not fully address. This report examines ethics review and oversight of three categories of AI-related health research: health-related data science using AI; research conducted with AI tools and technologies; and health-related research on AI tools and technologies. It analyses gaps in current ethical standards and oversight, challenges faced by research ethics committees, and measures to strengthen researchers’ adoption of ethical principles. The report also considers complementary roles for funders, data access mechanisms, scientific journals and publishers, regulatory agencies and other third parties. Particular attention is given to risks and inequities affecting low- and middle-income countries, including fairness, benefit sharing, data colonialism, ethics dumping, power imbalances and capacity-building. It further examines the benefits and risks of using AI in ethics review and provides non-exhaustive considerations for strengthening responsible research conduct and oversight.

Data ethics has emerged as a branch of the evolving ethical domain, particularly in addressing the ethical challenges associated with data processing, algorithms, and their applications. Ethical governance is crucial for sharing and utilizing medical scientific data. Ethical reviews serve as a practical embodiment of ethical principles, ensuring their implementation and protection within data-centric strategies and protocols. The author outlines the scope of data ethics norms, establishes a data ethics review framework based on international advancements, and analyses the applicability of basic ethical principles applied to data ethics.

Vaccination remains a cornerstone of public health, yet monitoring vaccine safety is essential to sustain confidence and guide policy. India’s large birth cohort, complex health system, and recurrent mass immunization campaigns necessitate a robust vaccine pharmacovigilance framework. This review systematically evaluates India’s vaccine safety surveillance landscape, including institutional mechanisms, surveillance models, causality assessment, and operational lessons from major immunization initiatives, to propose strategic priorities for 2025–2030.

Clinical trial (CT) safety surveillance is critical to protecting participants and ensuring reliable evidence on the safety and efficacy of new medical products. This is especially relevant in Africa, where CT activity remains limited, regulatory maturity varies, and drug safety surveillance systems are under-resourced despite considerable demographic advantages and genetic and cultural diversity. To assess the current state of CT safety surveillance in Africa, a landscape analysis of the systems for clinical trial safety data reporting, collation, and analysis was conducted. Despite challenges, opportunities exist to strengthen CT safety surveillance through digitization of reporting systems, harmonization of serious adverse event forms, regional collaboration, and capacity building for strengthening the ecosystem. The results of this landscape analysis provide a roadmap for building a coordinated model for pre-licensure safety monitoring across the continent.